Currently, life science research has officially entered the era of big data. Various high-throughput, low-cost new generation omics data and single molecule technologies have enabled life sciences to evolve from 'qualitative observation description' to 'quantitative detection and analysis'. The emergence of synthetic biology has developed technologies and related platforms for 'interpreting, writing, editing, and reconstructing' genomes, leading life sciences towards 'predictive programming' and 'regulation and reconstruction'. Third-generation genome editing technology based on the CRISPR/Cas9 system has been successfully applied to the genome modification of numerous species including animals, plants, and microorganisms, providing new ideas and methods for people to understand complex biological systems, targetedly modify and regulate genomes, and achieve precise treatment of diseases. This project is based on a series of algorithms developed by the application team in big data mining and relies on cloud computing platforms. The goal is to develop a precise and efficient gene editing computer-aided design and optimization system and promote its industrialization. There are mainly three innovations: First, based on traditional RNAi and CRISPR technology, we will develop a gene editing and sgRNA computer optimization design technology that integrates artificial intelligence and data mining. Second, by leveraging Hadoop and Spark technologies, we will build a cloud-based high-throughput data analysis and storage platform, providing users with mobile and client-side gene editing webserver and APP services. Third, we will fully consider the personalized design of sgRNA, providing services for subsequent precision medicine based on gene editing technology.